TY - JOUR
T1 - Multidimensional trait space informed by a mechanistic model of tree growth and carbon allocation
AU - Fell, Michael
AU - Barber, Jarrett
AU - Lichstein, Jeremy W.
AU - Ogle, Kiona
N1 - Funding Information:
This work was supported by an NSF grant to Ogle and Barber (DBI no. 0850361 and no. 1133366). We thank Jessica Guo, Drew Peltier, Edmund Ryan, and Heather Kropp for valuable input on the manuscript. We thank Fell’s remaining committee members for their support and feedback, including Janet Franklin, Thomas Day, and Kevin Hultine. Fell conducted all simulations and most data analyses and wrote the manuscript; Barber assisted with the MH algorithm, simulations, and data analysis and contributed to manuscript writing; Lich-stein provided the FIA data and contributed to manuscript writing; Ogle conceived the study, supervised simulations and analyses, and co-wrote the manuscript.
Funding Information:
This work was supported by an NSF grant to Ogle and Barber (DBI no. 0850361 and no. 1133366). We thank Jessica Guo, Drew Peltier, Edmund Ryan, and Heather Kropp for valuable input on the manuscript. We thank Fell’s remaining committee members for their support and feedback, including Janet Franklin, Thomas Day, and Kevin Hultine. Fell conducted all simulations and most data analyses and wrote the manuscript; Barber assisted with the MH algorithm, simulations, and data analysis and contributed to manuscript writing; Lichstein provided the FIA data and contributed to manuscript writing; Ogle conceived the study, supervised simulations and analyses, and co-wrote the manuscript.
Publisher Copyright:
© 2018 Fell et al.
PY - 2018/1
Y1 - 2018/1
N2 - Plant functional traits research has revealed many interesting and important patterns among morphological, physiological, and life-history traits and the environment. These are exemplified in trade-offs between groups of traits such as those embodied in the leaf and wood economics spectra. Inferences from empirical studies are often constrained by the correlative nature of the analyses, availability of trait data, and a focus on easily measured traits. However, empirical studies have been fundamental to modeling endeavors aiming to enhance our understanding of how functional traits scale up to affect, for example, community dynamics and ecosystem productivity. Here, we take a complementary approach utilizing an individual-based model of tree growth and mortality (the allometrically constrained growth and carbon allocation [ACGCA] model) to investigate the theoretical trait space (TTS) of North American trees. The model includes 32 parameters representing allometric, physiological, and anatomical traits, some overlapping leaf and wood economics spectra traits. Using a Bayesian approach, we fit the ACGCA model to individual tree heights and diameters from the USFS Forest Inventory and Analysis (FIA) dataset, with further constraints by literature-based priors. Fitting the model to 1.3 million FIA records—aggregated across individuals, species, and sites—produced a posterior distribution of traits leading to realistic growth. We explored this multidimensional posterior distribution (the TTS) to evaluate trait–trait relationships emerging from the ACGCA model, and compare these against empirical patterns reported in the literature. Only three notable bivariate correlations, among 496 possible trait pairs, were contained in the TTS. However, stepwise regressions uncovered a complicated structure; only a subset of traits—related to photosynthesis (e.g., radiation-use efficiency and maintenance respiration)—exhibited strong multivariate trade-offs with each other, while half of the traits—mostly related to allometries and construction costs—varied independently of other traits. Interestingly, specific leaf area was related to several rarely measured root traits. The trade-offs contained in the TTS generally reflect mass-balance (related to carbon allocation) and engineering (mostly related to allometries) trade-offs represented in the ACGCA model and point to potentially important traits that are under-explored in field studies (e.g., root traits and branch senescence rates).
AB - Plant functional traits research has revealed many interesting and important patterns among morphological, physiological, and life-history traits and the environment. These are exemplified in trade-offs between groups of traits such as those embodied in the leaf and wood economics spectra. Inferences from empirical studies are often constrained by the correlative nature of the analyses, availability of trait data, and a focus on easily measured traits. However, empirical studies have been fundamental to modeling endeavors aiming to enhance our understanding of how functional traits scale up to affect, for example, community dynamics and ecosystem productivity. Here, we take a complementary approach utilizing an individual-based model of tree growth and mortality (the allometrically constrained growth and carbon allocation [ACGCA] model) to investigate the theoretical trait space (TTS) of North American trees. The model includes 32 parameters representing allometric, physiological, and anatomical traits, some overlapping leaf and wood economics spectra traits. Using a Bayesian approach, we fit the ACGCA model to individual tree heights and diameters from the USFS Forest Inventory and Analysis (FIA) dataset, with further constraints by literature-based priors. Fitting the model to 1.3 million FIA records—aggregated across individuals, species, and sites—produced a posterior distribution of traits leading to realistic growth. We explored this multidimensional posterior distribution (the TTS) to evaluate trait–trait relationships emerging from the ACGCA model, and compare these against empirical patterns reported in the literature. Only three notable bivariate correlations, among 496 possible trait pairs, were contained in the TTS. However, stepwise regressions uncovered a complicated structure; only a subset of traits—related to photosynthesis (e.g., radiation-use efficiency and maintenance respiration)—exhibited strong multivariate trade-offs with each other, while half of the traits—mostly related to allometries and construction costs—varied independently of other traits. Interestingly, specific leaf area was related to several rarely measured root traits. The trade-offs contained in the TTS generally reflect mass-balance (related to carbon allocation) and engineering (mostly related to allometries) trade-offs represented in the ACGCA model and point to potentially important traits that are under-explored in field studies (e.g., root traits and branch senescence rates).
KW - Analysis (FIA)
KW - Forest inventory
KW - Individual-based model
KW - Markov chain Monte Carlo
KW - North American trees
KW - Plant functional traits
KW - Trait space
KW - Trait trade-offs
KW - Tree growth model
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U2 - 10.1002/ecs2.2060
DO - 10.1002/ecs2.2060
M3 - Article
AN - SCOPUS:85041241370
SN - 2150-8925
VL - 9
JO - Ecosphere
JF - Ecosphere
IS - 1
M1 - e02060
ER -